Technology for choosing a design for a computer data storage system having a prescribed reliability. The selection of a “matching storage system,” matching the prescribed reliability is based on computation of first and second reliability indicators.
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1. A computer-implemented method of designing a storage system having a prescribed reliability, the method comprising: formulating specifications for each candidate storage system design of a plurality of candidate storage system designs, wherein said each candidate storage system design includes a plurality of storage units, with each candidate storage system design including a set of storage devices; for each given candidate storage system design of the plurality of candidate storage system designs, determining a reliability of the given candidate storage system design by: computing a plurality of first reliability indicators for each distinct type of the storage units of the given candidate storage system design based on first parameters obtained from the specifications formulated for the given candidate storage system design, and computing a plurality of second reliability indicators for the given candidate storage system design, based on second parameters obtained from the first reliability indicators; identifying, within the plurality of second reliability indicators, matching indicator(s) that match the prescribed reliability and that correspond to a matching storage system design from the plurality of candidate storage system designs; and storing the specifications of the matching storage system design.
This invention relates to a computer-implemented method for designing a storage system with a specified reliability level. The problem addressed is the need to efficiently evaluate and select storage system designs that meet predefined reliability requirements, ensuring optimal performance and durability. The method involves generating multiple candidate storage system designs, each consisting of multiple storage units and a set of storage devices. For each candidate design, the method calculates reliability metrics in two stages. First, it computes a set of reliability indicators for each distinct type of storage unit within the design, using parameters derived from the design specifications. Second, it calculates additional reliability indicators for the entire storage system based on the initial reliability metrics. The method then compares these reliability indicators against the prescribed reliability target. If a candidate design meets the reliability criteria, its specifications are stored as a valid solution. This approach systematically evaluates different storage configurations to identify those that achieve the desired reliability, streamlining the design process for storage systems. The method ensures that only designs meeting the specified reliability standards are selected, improving system reliability and reducing the risk of failures.
2. The computer-implemented method according to claim 1 , wherein: the first reliability indicators and the second reliability indicators comprise, each, a mean time to data loss, or MTTDL, and an expected annual fraction of data loss, or EAFDL, and the first reliability indicators additionally comprise an expected amount of data lost conditioned on a fact that a data loss occurred, or EADLC.
This invention relates to a computer-implemented method for assessing and comparing data reliability in distributed storage systems. The method addresses the challenge of evaluating and quantifying data loss risks in storage systems, particularly in environments where data is distributed across multiple nodes or devices. The core problem is the lack of standardized metrics to measure and compare the reliability of different storage configurations or systems. The method involves calculating and comparing reliability indicators for at least two different storage configurations or systems. These indicators include a mean time to data loss (MTTDL), which represents the average time before a data loss event occurs, and an expected annual fraction of data loss (EAFDL), which quantifies the proportion of data expected to be lost annually. Additionally, the method includes an expected amount of data lost conditioned on a fact that a data loss occurred (EADLC), which measures the severity of data loss when it happens. The method allows for the comparison of these indicators between different storage configurations to determine which system provides better reliability. This approach helps users or administrators make informed decisions about storage system design, redundancy strategies, and risk management. The method is particularly useful in distributed storage environments where data is replicated or erasure-coded across multiple nodes, as it provides a comprehensive assessment of reliability under different failure scenarios.
3. The computer-implemented method according to claim 2 , the method further comprising: for each given candidate storage system design of the plurality of candidate storage system designs, estimating an equivalent memory storage capacity at risk of each of the storage units of the given candidate storage system design based on the EADLC computed for each distinct type of the storage units; and wherein the second parameters are obtained based on the MTTDL as computed for each distinct type of the storage units and the equivalent memory storage capacity at risk as estimated for each of the candidate storage system design of the plurality of candidate storage system designs.
The invention relates to a computer-implemented method for evaluating storage system designs, particularly focusing on reliability and capacity risk assessment. The method addresses the challenge of optimizing storage system configurations by quantifying the reliability and potential data loss risks associated with different storage unit types and designs. The method involves analyzing a plurality of candidate storage system designs, each comprising multiple storage units of distinct types. For each candidate design, the method estimates the equivalent memory storage capacity at risk for each storage unit based on the estimated annualized data loss cost (EADLC) computed for each distinct storage unit type. The EADLC represents the financial impact of potential data loss over a year, factoring in the likelihood of failure and the value of stored data. Additionally, the method computes the mean time to data loss (MTTDL) for each distinct storage unit type, which indicates the average time before data loss occurs. The second set of parameters, used for further analysis or decision-making, is derived from the MTTDL and the estimated equivalent memory storage capacity at risk across all candidate designs. This approach enables a comprehensive assessment of storage system reliability, helping to identify the most cost-effective and resilient configurations.
4. The computer-implemented method according to claim 3 , wherein: the first parameters include a mean time to failure, or MTTF, of each of the storage devices; and the second parameters include an MTTF of each distinct type of the storage units, wherein the MTTF of each distinct type of the storage units is obtained by setting it equal to the MTTDL as computed for said each distinct type of the storage units.
This invention relates to a computer-implemented method for managing storage systems, specifically focusing on reliability assessment and failure prediction. The method addresses the challenge of accurately estimating the mean time to failure (MTTF) of storage devices and storage units within a storage system to improve reliability and maintenance planning. The method involves analyzing storage devices and storage units, where storage units are distinct types or configurations of storage components. The first set of parameters includes the MTTF of individual storage devices, which quantifies their expected operational lifespan. The second set of parameters includes the MTTF of each distinct type of storage unit, which is derived by equating it to the mean time to data loss (MTTDL) for that specific type of storage unit. The MTTDL represents the average time before data loss occurs due to failures within the storage unit. By correlating the MTTF of storage units with their MTTDL, the method provides a standardized way to assess and compare the reliability of different storage configurations. This approach enables more accurate failure prediction, proactive maintenance, and optimized storage system design. The method ensures that reliability metrics are consistently applied across diverse storage technologies and configurations, enhancing overall system dependability.
5. The computer-implemented method according to claim 4 , wherein: said first parameters include, in addition to the MTTF of each of the storage devices of each given candidate storage system design of the plurality of candidate storage system designs: a number of storage devices of the given candidate storage system design; and a memory storage capacity of each of the storage devices of the candidate storage system designs.
This invention relates to optimizing storage system designs by evaluating multiple candidate designs based on reliability and capacity metrics. The method involves analyzing a plurality of candidate storage system designs to determine the most efficient configuration. Each candidate design is assessed using first parameters that include the mean time to failure (MTTF) of each storage device within the design, the total number of storage devices in the design, and the memory storage capacity of each storage device. These parameters are used to compare and rank the candidate designs, enabling selection of the most reliable and capacity-optimized configuration. The method ensures that storage systems are designed with both reliability and storage capacity in mind, addressing the challenge of balancing these factors in large-scale storage deployments. By incorporating MTTF, device count, and individual storage capacity, the approach provides a comprehensive evaluation framework for storage system architects. This allows for data-driven decisions that enhance system longevity and performance while meeting storage requirements. The technique is particularly useful in environments where storage reliability and capacity planning are critical, such as data centers or enterprise storage solutions.
6. The computer-implemented method according to claim 5 , wherein: said first parameters additionally include parameters relating to a data placement scheme of data throughout the storage devices of the storage units.
This invention relates to a computer-implemented method for managing data storage in a distributed storage system. The method addresses the challenge of efficiently distributing and placing data across multiple storage units to optimize performance, reliability, and resource utilization. The system involves analyzing storage devices within storage units to determine optimal data placement schemes, which are then used to distribute data in a way that balances load, minimizes latency, and ensures fault tolerance. The method includes evaluating first parameters that define the characteristics of the storage devices, such as capacity, speed, and availability. These parameters are used to determine how data should be allocated across the storage units. Additionally, the first parameters include data placement schemes that dictate how data is distributed throughout the storage devices. These schemes may involve techniques like replication, erasure coding, or distributed hashing to ensure data redundancy and availability. The method further involves dynamically adjusting the data placement based on real-time conditions, such as device performance, network latency, or storage capacity changes. By continuously monitoring and optimizing the placement of data, the system ensures efficient use of storage resources while maintaining high availability and reliability. This approach is particularly useful in large-scale storage environments where data distribution must be carefully managed to prevent bottlenecks and ensure seamless access.
7. The computer-implemented method according to claim 6 , wherein: said data placement scheme is one of a symmetric data placement scheme, a clustered data placement scheme, and a declustered data placement scheme.
This invention relates to data storage systems and methods for optimizing data placement across storage devices to improve performance and reliability. The problem addressed is inefficient data distribution, which can lead to bottlenecks, uneven wear, or increased risk of data loss in storage systems. The method involves selecting a data placement scheme to distribute data across multiple storage devices. The placement scheme can be symmetric, where data is evenly distributed across all devices, clustered, where data is concentrated on a subset of devices, or declustered, where data is spread in a non-uniform manner to balance load and redundancy. The method also includes determining storage device characteristics, such as capacity, performance, and reliability, to inform the placement scheme selection. This ensures optimal use of storage resources while minimizing risks like device failure or performance degradation. The invention is particularly useful in distributed storage systems, such as cloud storage or enterprise data centers, where efficient data placement is critical for scalability and fault tolerance.
8. The computer-implemented method according to claim 7 , wherein: the plurality of candidate storage system designs includes at least one candidate storage system design characterized by each of the following types of data placement schemes: symmetric, clustered and declustered.
The invention relates to optimizing storage system designs for data placement schemes. The problem addressed is efficiently evaluating and selecting storage system configurations that balance performance, reliability, and cost by considering different data placement strategies. The method involves generating a plurality of candidate storage system designs, each incorporating at least one of three distinct data placement schemes: symmetric, clustered, and declustered. Symmetric placement distributes data evenly across storage devices, clustered placement groups related data on the same devices, and declustered placement spreads data to minimize overlap. The method analyzes these designs to determine optimal configurations based on predefined criteria such as access patterns, fault tolerance, and resource utilization. By comparing the performance and reliability of each scheme, the system identifies the most suitable design for a given workload. This approach ensures that storage systems are tailored to specific requirements, improving efficiency and reducing operational costs. The invention is particularly useful in large-scale data centers and cloud storage environments where data distribution strategies significantly impact overall system performance.
9. The computer-implemented method according to claim 6 , wherein: said first parameters additionally include a rebuild bandwidth of each of the storage devices.
A computer-implemented method for managing storage systems addresses the challenge of optimizing data rebuild operations in distributed storage environments. The method focuses on improving efficiency and performance during data reconstruction, particularly in scenarios where storage devices fail or require maintenance. The technique involves dynamically adjusting rebuild operations based on system conditions, including the available bandwidth of individual storage devices. The method monitors and utilizes first parameters, which include the rebuild bandwidth of each storage device, to determine optimal rebuild strategies. By considering the bandwidth constraints of each device, the system can allocate resources more effectively, reducing the impact on overall system performance. This approach ensures that rebuild operations do not overwhelm the storage infrastructure, maintaining data availability and minimizing downtime. Additionally, the method may incorporate other parameters, such as device health, network latency, and workload priorities, to further refine rebuild decisions. The system dynamically adjusts rebuild tasks based on these factors, ensuring that critical data is prioritized and resources are allocated efficiently. This adaptive approach enhances system reliability and performance, particularly in large-scale storage environments where multiple devices may be involved in rebuild operations. The technique is applicable to various storage systems, including distributed file systems, cloud storage, and enterprise storage solutions.
10. The computer-implemented method according to claim 9 , wherein: said first parameters additionally include parameters of a protection scheme of the storage devices of the storage units.
This invention relates to a computer-implemented method for managing storage systems, specifically addressing the challenge of optimizing storage performance and reliability by incorporating protection scheme parameters into storage device management. The method involves analyzing and adjusting storage device parameters, including those related to protection schemes, to enhance data integrity and system efficiency. Protection schemes may include redundancy configurations, error correction mechanisms, or encryption protocols, which are critical for safeguarding data against failures or unauthorized access. By integrating these parameters into the storage management process, the system can dynamically adapt to changing conditions, ensuring consistent performance while maintaining high levels of data protection. The method leverages real-time monitoring and automated adjustments to optimize storage operations, reducing the risk of data loss and improving overall system reliability. This approach is particularly valuable in environments where data integrity and availability are paramount, such as enterprise storage systems or cloud-based data centers. The invention provides a comprehensive solution for balancing performance and protection in storage management, addressing a key need in modern data storage technologies.
11. The computer-implemented method according to claim 4 , wherein: said second parameters additionally include a number of the storage units.
A system and method for managing data storage in a distributed computing environment addresses the challenge of efficiently allocating and utilizing storage resources across multiple storage units. The invention provides a technique for optimizing storage allocation by dynamically adjusting parameters based on system conditions. The method involves determining a set of first parameters related to storage requirements, such as data size, access patterns, and redundancy needs. A second set of parameters, including the number of storage units available, is used to configure the storage system. The system evaluates these parameters to distribute data across the storage units in a manner that balances load, minimizes latency, and ensures data durability. By incorporating the number of storage units as a configurable parameter, the system can adapt to changes in storage capacity, such as adding or removing units, without manual intervention. This dynamic adjustment improves resource utilization and scalability while maintaining performance and reliability. The method is particularly useful in cloud storage and distributed file systems where storage resources are frequently reconfigured.
12. The computer-implemented method according to claim 11 , wherein: said second parameters additionally include parameters related to a protection scheme of the storage units of said each candidate.
This invention relates to a computer-implemented method for optimizing storage unit selection in a distributed storage system. The method addresses the challenge of efficiently selecting storage units based on multiple criteria, including performance, reliability, and protection schemes, to ensure data integrity and availability. The method involves evaluating candidate storage units using a set of first parameters, such as performance metrics and reliability indicators, to identify a subset of suitable units. A second set of parameters is then applied to further refine the selection, including parameters related to the protection scheme of each candidate storage unit. The protection scheme parameters may include redundancy levels, encryption methods, or fault tolerance mechanisms, ensuring that the selected storage units meet specified security and durability requirements. By incorporating protection scheme parameters into the selection process, the method enhances the ability to choose storage units that not only meet performance and reliability standards but also adhere to specific data protection policies. This approach improves the overall robustness and security of the distributed storage system, making it suitable for applications requiring high data integrity and availability. The method dynamically adjusts the selection criteria based on real-time conditions, ensuring optimal storage unit allocation under varying operational constraints.
13. The computer-implemented method according to claim 1 , wherein: the first reliability indicators are computed by a set of first functions that respectively take the first parameters as arguments; the second reliability indicators are computed by a set of second functions that respectively take the second parameters as arguments; and each of the first functions and the second functions are non-invertible functions.
This invention relates to a computer-implemented method for evaluating reliability indicators in a system where data is processed through non-invertible functions. The method addresses the challenge of ensuring accurate and secure reliability assessments in systems where data transformations must be irreversible to prevent reverse engineering or unauthorized access. The method involves computing first reliability indicators using a set of first functions, each taking specific first parameters as inputs. Similarly, second reliability indicators are computed using a set of second functions, each taking specific second parameters as inputs. Both the first and second functions are non-invertible, meaning they cannot be reversed to retrieve the original input parameters from their outputs. This ensures that the reliability indicators remain secure and resistant to tampering or reconstruction. The use of non-invertible functions enhances security by preventing the reconstruction of sensitive input parameters from the computed reliability indicators. This approach is particularly useful in applications where data integrity and confidentiality are critical, such as in secure authentication systems, cryptographic protocols, or privacy-preserving data analysis. The method ensures that reliability assessments are both accurate and resistant to reverse engineering, providing a robust solution for systems requiring secure and irreversible data transformations.
14. The computer-implemented method according to claim 1 , wherein: at least one of the storage units of one or more of the candidate storage system designs includes a storage pod, the storage devices of which are hard disk drives.
A computer-implemented method optimizes storage system design by evaluating candidate designs based on performance, cost, and reliability metrics. The method involves generating multiple candidate storage system designs, each comprising one or more storage units, where each storage unit includes multiple storage devices. The storage devices may include different types, such as solid-state drives (SSDs) or hard disk drives (HDDs). The method simulates workloads on these candidate designs to assess their performance, cost, and reliability. Based on the simulation results, the method selects an optimal storage system design that meets predefined criteria. In some implementations, at least one storage unit in one or more of the candidate designs includes a storage pod, where the storage devices within the pod are hard disk drives. This configuration allows for flexible storage system architectures that can be tailored to specific workload requirements, balancing cost, performance, and reliability. The method helps organizations design efficient storage systems by leveraging automated evaluation and optimization techniques.
15. The computer-implemented method according to claim 14 , wherein: each of the storage units of each of the candidate storage systems is a storage pod including hard disk drives.
This invention relates to computer-implemented methods for managing data storage in distributed systems, specifically addressing the challenge of efficiently selecting and utilizing storage resources in large-scale environments. The method involves evaluating multiple candidate storage systems to determine their suitability for storing data, with a focus on optimizing performance, cost, and reliability. Each candidate storage system comprises multiple storage units, and the method assesses these units to identify the most appropriate configuration for data storage. The storage units within each candidate system are storage pods, each containing hard disk drives (HDDs). The method analyzes these pods to determine their capacity, performance characteristics, and availability, ensuring that the selected storage system meets predefined criteria for data storage. This evaluation may include assessing factors such as read/write speeds, latency, and fault tolerance to ensure reliable data access and durability. The method then selects the optimal storage system and configures the storage pods within it to store data efficiently, balancing factors like cost, performance, and redundancy. By dynamically evaluating and selecting storage systems based on their storage pod configurations, this approach improves data storage efficiency in distributed environments, particularly in scenarios where multiple storage options are available. The use of HDD-based storage pods ensures cost-effective and scalable storage solutions while maintaining performance and reliability.
16. The computer-implemented method according to claim 1 , wherein: each of the storage units is assumed to be configured in said each candidate as a redundant array of independent disks.
This invention relates to data storage systems, specifically methods for optimizing storage configurations in distributed computing environments. The problem addressed is the inefficient allocation of storage resources in large-scale systems, leading to suboptimal performance, reliability, or cost. The solution involves evaluating multiple candidate storage configurations to determine an optimal arrangement based on predefined criteria such as performance, redundancy, or cost. The method involves analyzing a plurality of storage units, each configured as a redundant array of independent disks (RAID) to enhance data reliability and availability. The RAID configuration ensures that data is distributed across multiple disks, providing fault tolerance and improved read/write performance. The system evaluates different candidate configurations by simulating their behavior under various workloads and failure scenarios. This evaluation helps identify the most efficient storage arrangement that meets the desired performance and reliability requirements while minimizing costs. The method further includes dynamically adjusting the storage configuration based on real-time monitoring of system performance and resource utilization. This adaptive approach ensures that the storage system remains optimized as workloads and requirements change over time. The invention is particularly useful in cloud computing, data centers, and other environments where large-scale storage systems are deployed. By optimizing storage configurations, the method improves system efficiency, reduces downtime, and lowers operational costs.
17. A computer program product of designing a storage system having a prescribed reliability, the computer program product comprising: a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause a processor(s) set to perform at least the following operations: formulating specifications for each candidate storage system design of a plurality of candidate storage system designs, wherein said each candidate storage system design includes a plurality of storage units, with each candidate storage system design including a set of storage devices, for each given candidate storage system design of the plurality of candidate storage system designs, determining a reliability of the given candidate storage system design by: computing a plurality of first reliability indicators for each distinct type of the storage units of the given candidate storage system design based on first parameters obtained from the specifications formulated for the given candidate storage system design, and computing a plurality of second reliability indicators for the given candidate storage system design, based on second parameters obtained from the first reliability indicators; identifying, within the plurality of second reliability indicators, matching indicator(s) that match the prescribed reliability and that correspond to a matching storage system design from the plurality of candidate storage system designs, and storing the specifications of the matching storage system design.
This invention relates to a computer program product for designing a storage system with a prescribed reliability. The problem addressed is the need to efficiently evaluate and select storage system designs that meet specific reliability requirements. The solution involves generating multiple candidate storage system designs, each comprising a set of storage devices and a plurality of storage units. For each candidate design, the system computes reliability indicators in two stages. First, it calculates a set of first reliability indicators for each distinct type of storage unit in the design, using parameters derived from the design specifications. Second, it computes a set of second reliability indicators for the entire candidate design, based on the first reliability indicators. The system then identifies any second reliability indicators that match the prescribed reliability and corresponds to a matching storage system design. The specifications of this matching design are stored for further use. This approach automates the evaluation of storage system reliability, ensuring that only designs meeting the required reliability thresholds are selected.
18. The computer program product according to claim 17 , wherein: the first reliability indicators and the second reliability indicators comprise, each, a mean time to data loss, or MTTDL, and an expected annual fraction of data loss, or EAFDL, and the first reliability indicators additionally comprise an expected amount of data lost conditioned on a fact that a data loss occurred, or EADLC.
This invention relates to evaluating and comparing the reliability of data storage systems, particularly in distributed or redundant storage environments. The problem addressed is the need for standardized metrics to assess and compare the reliability of different storage configurations, ensuring data integrity and availability over time. The invention provides a method for generating and using reliability indicators to quantify data loss risks. These indicators include Mean Time to Data Loss (MTTDL), which measures the average time before data loss occurs, and Expected Annual Fraction of Data Loss (EAFDL), which estimates the fraction of data lost annually. Additionally, the Expected Amount of Data Lost Conditioned on a Data Loss Occurrence (EADLC) is used to quantify the severity of data loss when it happens. The method involves calculating these indicators for at least two different storage configurations or systems, allowing for a comparative analysis. The indicators are derived from system parameters such as redundancy levels, failure rates, and repair times. By comparing these metrics, users can select the most reliable storage configuration for their needs, balancing between cost, performance, and data protection. The invention is particularly useful in cloud storage, distributed databases, and other systems where data reliability is critical.
19. The computer program product according to claim 18 , wherein the computer code further includes instructions for causing the processor(s) set to perform the following operation(s): for each given candidate storage system design of the plurality of candidate storage system designs, estimating an equivalent memory storage capacity at risk of each of the storage units of the given candidate storage system design based on the EADLC computed for each distinct type of the storage units; and wherein the second parameters are obtained based on the MTTDL as computed for each distinct type of the storage units and the equivalent memory storage capacity at risk as estimated for each of the candidate storage system design of the plurality of candidate storage system designs.
This invention relates to optimizing storage system designs by evaluating reliability metrics. The technology addresses the challenge of assessing and mitigating data loss risks in storage systems, particularly in distributed or redundant storage architectures. The system computes an Equivalent Annualized Data Loss Cost (EADLC) for each distinct type of storage unit within a candidate storage system design. For each candidate design, the system estimates the equivalent memory storage capacity at risk for each storage unit based on the computed EADLC. The system then uses these estimates, along with the Mean Time to Data Loss (MTTDL) for each storage unit type, to derive second parameters that quantify the reliability and risk profile of each candidate design. This approach enables comparative analysis of different storage configurations to identify the most reliable and cost-effective design options. The method leverages probabilistic models to account for varying failure rates and redundancy strategies, ensuring that the storage system design minimizes data loss risks while optimizing resource utilization. The invention is particularly useful in large-scale storage deployments where reliability and cost efficiency are critical.
20. A computer system comprising: a processor(s) set; a set of storage device(s); and computer code stored collectively in the set of storage device(s), with the computer code including data and instructions to cause the processor(s) set to perform at least the following operations: formulating specifications for each candidate storage system design of a plurality of candidate storage system designs, wherein said each candidate storage system design includes a plurality of storage units, with each candidate storage system design including a set of storage devices, for each given candidate storage system design of the plurality of candidate storage system designs, determining a reliability of the given candidate storage system design by: computing a plurality of first reliability indicators for each distinct type of the storage units of the given candidate storage system design based on first parameters obtained from the specifications formulated for the given candidate storage system design, and computing a plurality of second reliability indicators for the given candidate storage system design, based on second parameters obtained from the first reliability indicators; identifying, within the plurality of second reliability indicators, matching indicator(s) that match the prescribed reliability and that correspond to a matching storage system design from the plurality of candidate storage system designs, and storing the specifications of the matching storage system design.
This invention relates to a computer system for evaluating and selecting storage system designs based on reliability metrics. The system addresses the challenge of optimizing storage system configurations to meet prescribed reliability requirements while considering various storage unit types and their failure characteristics. The computer system includes processors and storage devices containing executable code. The code performs operations to generate specifications for multiple candidate storage system designs, each comprising multiple storage units and a set of storage devices. For each candidate design, the system computes reliability indicators in two stages. First, it calculates a set of first reliability indicators for each distinct storage unit type within the design, using parameters derived from the design specifications. Second, it computes a set of second reliability indicators for the entire design, based on the first reliability indicators and additional parameters. The system then identifies any second reliability indicators that match the prescribed reliability thresholds, corresponding to a matching storage system design. The specifications of this design are stored for further use. This approach enables automated evaluation of storage system reliability by systematically analyzing different configurations and identifying those that meet predefined reliability criteria, facilitating optimal storage system selection.
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January 27, 2021
February 8, 2022
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